Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Intelligent Structuring and Reducing of Association Rules with Formal Concept Analysis
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
A survey of data mining and knowledge discovery software tools
ACM SIGKDD Explorations Newsletter
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
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In this paper, we are interested of the end-user for who have been defined different approaches for Knowledge Discovery in Database (KDD) One of the problems met with these approaches is the big number of generated rules that are not easily assimilated by the human brain In this paper, we discuss these problems and we propose a pragmatic solution by (1) proposing a new approach for KDD through the fusion of conceptual clustering, fuzzy logic and formal concept analysis, and by (2) defining an Expert System (ES) allowing the user to easily exploit all generated knowledge in the first step Indeed, this ES can help the user to give semantics of data and to optimize the research of information This solution is extensible; the user can choose the fuzzy method of classification according to the domain of his data and his needs or the Inference Engine for the ES.